Become a data-driven marketing expert by mastering concepts around analytics lifecycle, data infrastructure, customer lifecycle and digital trends while going through global retail banking case studies.
Learning Objectives - In this module you will understand the scope of analytics applications at a retail bank and the underlying processes involved. You will also learn about the various activities around analytics. Develop a sound foundation of analytics frameworks. Learn about best practices in analytics and also understand latest trends around analytics.
Topics - Analytics objectives, Analytics data stack, Analytics lifecycle, Analytics process cycles, Analytics algorithms stack, Data visualization, Context awareness, Analytics best practices, CRISP-DM methodology.
Learning Objectives - In this module you will understand different stages of the customer lifecycle, Marketing challenges across different stages of the customer lifecycle, Best practices in managing these challenges, How to use analytics to address these challenges and Undertake a case study of a Taiwanese bank.
Topics - Retail banking objectives, Customer lifecycle, Analytics applications across the customer lifecycle, Levers, Analytics objectives and trade-offs, Segment marketing, Partner agencies, ROI models
Learning Objectives - In this module you will understand the various types of data needed at a retail bank, Infrastructure required to manage data and learn about challenges and best practices in managing data.
Topics - Challenges of big data, Different types of data, Data life cycle Logical data models, Data cleansing, Unstructured data processing, Single view of the customer, Single row per customer, Platform components required to process data, Requisite processes.
Learning Objectives - In this module you will understand the various types of channels and their implications on data-driven marketing. Learn about customer touch-points and how they can be leveraged. Appreciate best practices around analytics and channel management.
Topics - Channel purposes, Types of channels, Channel throughput, Channel infrastructure, Campaign execution challenges, Omni-channel perspective, Use of social media channels.
Learning Objectives - In this module you will understand how to run data-driven acquisition programs, Best practices around analytics in the acquisition space, understand the differences between prospecting and onboarding and also learn about best practices around digital onboarding. Carry out a case study of an Indonesian bank.
Topics - Prospecting, Onboarding, Analytics capabilities for prospect analytics, Response models, Activation strategies, Digital activation best and worst practices.
Learning Objectives - In this module you will understand how to run data driven usage management programs, Explore best practices around analytics in the usage management space. Learn about challenges while implementing offers. Perform a case study of a Thai bank and Chinese bank.
Topics - Analytics capabilities required, Sample usage increase programs, Offer glut, Offer fulfillment and tracking.
Learning Objectives - In this module you will understand the customer journey and define customer experience. Learn about the benefits of having a good customer experience, How to run data-driven customer experience management programs, best practices around analytics in the customer experience management space and also understand best practices of customer experience in digital banking.
Topics - Customer journey and analytics, Customer experience processes, Customer trust principles, Analytics capabilities required for customer experience, Analytics capabilities required for customer satisfaction, Analytics for the end customer, Personal financial management, Technology shifts, Design thinking, Testing options, Digital customer experience sensors and actuators.
Learning Objectives - In this module you will understand how to run data driven upsell and cross sell programs. Learn about best practices of analytics in the upsell and cross sell space, tactics to increase customer penetration, approaches to Bancassurance perform a case study of an Indian bank and Chinese bank.
Topics - Upselling and cross selling processes, Tactics to increase customer penetration, "Incoming call is your best bet", Next best offer analytics, Case study: Card upgrade program, Case study: Cross selling credit cards to savings accounts, Case study: Cross Selling mutual funds to savings account customers, Cross sell between corporate and individual accounts, Bancassurance approaches.
Learning Objectives - Understand how to run data-driven retention and loyalty management programs, Approaches to building retention strategies, trends in social media marketing. Learn about best practices of analytics in the retention and loyalty management space. Undertake a case study of an Indian bank
Topics - Retention and loyalty processes, Factors affecting, Customer loyalty, Analytics capability for loyalty analytics, Attrition types and retention strategies, Case Study: Attrition model, Advocacy analytics, Social Media Marketing.
Learning Objectives - Understand practical challenges in implementing data driven programs. Learn about basic principles driving IT infrastructure of digital banking and also you will learn how to manage these challenges.
Topics - McKinsey core beliefs on big data, Data privacy, IT principles for digital banking, Architecture blocks for digital banking, "Know your business", Data preparation groundwork, "Analytics is more art than science", Common improvement areas at banks.
The two immutable truths of retail banking are:
However, increased competition, advent of technology and proliferation of channels to service the customer, have led to the following:
Increased usage of impersonal electronic services
Access to low cost electronic services has led to banks operating with a widespread and diffuse customer base. This has in turn led to:
Low customer intimacy level along with the security issues related to electronic services like Internet banking increase the potential for fraudulent activities like money laundering.
Shrinking opportunity window to know and influence Customers. This has led to reduced time window for marketing products and services. The graphic shows the relevance of an event (such as a promotional event) to a customer as a function of time elapsed after the event. This shows that customer interest peaks and falls rapidly. This makes it absolutely necessary for banks to optimally leverage all available customer touch points so as to be able to influence the customer.
In short, these points amount to a reduced knowledge of customer behavior. Banks worldwide have responded to this challenge by using modeling and decision theory based solutions. Some of the issues addressed are: assessing life cycle value of customers, designing focused marketing campaigns to reduce cost and improve retention, improving in-bank service levels, modeling credit risks and scientifically determine risk capital and so on